Quantitative single-cell rna-seq with unique molecular identifers

这篇文章论证了 scRNA-seq 使用UMI来计算基因表达量的合理性和优势。

这里主要研究如何分析 scRNA-seq 的数据，如何处理ERCC和UMI。

 

背景：

however, losses in cdna synthesis and bias in cdna amplifcation lead to severe quantitative errors.

单细胞测序，每个细胞内的mRNA的量都比较少，在合成cDNA时必然会有困难，所以就需要扩增，否则根本就检测不到mRNA，但扩增会导致定量的问题，因为你无法精准的控制扩增的量，这样你计算的表达量就是不准的。

The two main challenges in single-cell RNA-seq are the efficiency of cDNA synthesis (which sets the limit of detection) and the amplification bias (which reduces quantitative accuracy).

单细胞测序的主要问题是：1.cDNA合成；2.扩增偏差

In contrast, standard RNA-seq uses relative measures such as reads per kilobase per million reads

(RPKM), which mask differences in total mRNA content.

常规RNA-seq使用RPKM或FPKM来定量，这是相对定量；而UMI就是绝对定量。

so that the number of identical mRNA molecules is expected to be <100 for most genes.

UMI的原理，一般UMI就只有5bp，也就只有4的5次方种，1024种；UMI是一种随机序列，并不是一种特异标记，它并不是为了给每一个mRNA分子一个特殊标记。

To ensure that successfully generated cDNA molecules are sequenced, it is crucial to sequence to a sufficient depth after amplification.

单细胞必须保证有足够的测序深度

 

Pairwise correlation coefficients calculated